Omics experiments in Iran, a review in endocrine and metabolism disorders studies

Appraisal of SARS-CoV-2 mutations and their impact on vaccination efficacy: an overview

With the unexpected emergence of the novel 2019 Wuhan coronavirus, the world was faced with a sudden uproar that quickly shifted into a serious life-threatening pandemic. Affecting the lives of the global population and leaving drastic damage in various sections and systems, several measures have been constantly taken to tackle down this crisis. For instance, numerous vaccines have been developed in the past two years, some of which have been granted emergency use, thus providing sufficient immunity to the vaccinated individuals. However, the appearance of newly emerged SARS-CoV-2 variants with accelerated transmission and fatality has led the world towards another pandemic. Having undergone various mutations in genomic and/or amino acid profiles, some of the emerged variants of concern (VOCs) including Alpha, Beta, Gamma, and Delta have displayed immune evasion and pathogenicity even in the vaccinated population, hence raising concerns regarding the efficacy of current vaccines against new VOCs of COVID-19. Therefore, genomic investigations of SARS-CoV-2 mutations are expected to provide valuable insight into the evolution of SARS-CoV-2, while also determining the impact of different mutations on infection severity. This study was constructed with the aim of shining light on recent advances regarding mutations in major COVID-19 VOCs, as well as vaccination efficacy against those VOCs.

Circulating amino acids and acylcarnitines correlated with different CAC score ranges in diabetic postmenopausal women using LC-MS/MS based metabolomics approach

BACKGROUND

Diabetes mellitus (DM) and its cardiovascular disease (CVD) complication are among the most frequent causes of death worldwide. However, the metabolites linking up diabetes and CVD are less understood. In this study, we aimed to evaluate serum acylcarnitines and amino acids in postmenopausal women suffering from diabetes with different severity of CVD and compared them with healthy controls.

METHODS

Through a cross-sectional study, samples were collected from postmenopausal women without diabetes and CVD as controls (n = 20), patients with diabetes and without CVD (n = 16), diabetes with low risk of CVD (n = 11), and diabetes with a high risk of CVD (n = 21) referred for CT angiography for any reason. Metabolites were detected by a targeted approach using LC-MS/MS and metabolic -alterations were assessed by applying multivariate statistical analysis. The diagnostic ability of discovered metabolites based on multivariate statistical analysis was evaluated by ROC curve analysis.

RESULTS

The study included women aged from 50-80 years with 5-30 years of menopause. The relative concentration of C14:1, C14:2, C16:1, C18:1, and C18:2OH acylcarnitines decreased and C18 acylcarnitine and serine increased in diabetic patients compared to control. Besides, C16:1 and C18:2OH acylcarnitines increased in high-risk CVD diabetic patients compared to no CVD risk diabetic patients.

CONCLUSION

Dysregulation of serum acylcarnitines and amino acids profile correlated with different CAC score ranges in diabetic postmenopausal women. (Ethic approval No: IR.TUMS.EMRI.REC.1399.062).

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.